In this direction, model-based reinforcement learning (MBRL) approximates iteratively the dynamics model of the environment while planning actions through trajectory optimization [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, commonly using Model Predictive Control (MPC) [7]}, [8]}, [9]}. Though sample-efficient, MBRL has been heavily impeded by the bias in the learned model–as optimal control methods will generally continue to exploit known regions of the learned dynamics without exploring outwards to new, unrevealed states [10]}, [11]}, [12]}. Model-free reinforcement learning (MFRL), on the other hand, has achieved impressive results in learning complex skills through deep neural networks [13]}, [14]}, [15]}, [16]}, [17]}, offering high performance. However, unlike their MB counterpart, the lack of an internal model makes MF algorithms data-hungry, suffering from poor use of samples; as the complexity of the task increases, so does the number of samples required to learn the optimal policy [18]}, [19]}, [20]}. This disadvantage makes modern deep MFRL methods inappropriate for learning tasks on physical robots [21]}, [22]}, [23]}, as the increased amount of necessary interactions is likely to damage the system.
